LASUA: A Lightweight Authentication Scheme with User Anonymity for IoT-Enabled Mobile Cloud
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Mobile Cloud Computing (MCC) also known as on-demand computing uses cloud computing to deliver applications to mobile devices. This new computational paradigm model which plays a big part in the Internet of Things (IoT), has increased its popularity even more during Covid-19 pandemic and became a necessity when schools, businesses and hospitals must work remotely. We can access and process remote data which are stored over the cloud server in real-time by connecting to a wireless network. For accessing any cloud server, a mutual authentication and key agreement between a mobile user and a cloud server provider is required. However, existing authentication schemes for MCC fail to provide user anonymity, server anonymity and user untraceability. Therefore, we propose a Lightweight Authentication Scheme with User Anonymity (LASUA) which artfully employs Elliptic Curve Cryptography (ECC), random number, time stamps, one-way hash functions, concatenation, XOR operations and fuzzy extractor for biometric to enable various security features including anonymity and resistance against various attacks. LASUA utilises the hardness of ECC to provide top-notch security with low computation and communication cost, a perfect solution for resource constrained devices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.007 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it